
import pandas as pd


df =pd.read_csv('onlinedeliverydata.csv')
df.head()


pd.set_option('max_columns', 100) #Display up to 100 columns you can also do it for rows !
#pd.set_option("display.max_rows", 100) 
df.head(5)


df.describe()


import numpy as np

# Note:
#   pd.pivot table ( df , categorical columns as index ,
#     numerical columns as values , aggregation functions as list of func )

delivery_pivot1=pd.pivot_table(df,index=["Gender","Marital Status"],
                               values=['Age','Family size'],
                               aggfunc=[np.mean,len], margins=True)
delivery_pivot1


delivery_pivot2=pd.pivot_table(df,index=["Gender"],
                               values=['Age','Family size'],
                               aggfunc=[np.median,len], margins=True)
delivery_pivot2


delivery_pivot3=pd.pivot_table(df,index=["Occupation","Monthly Income"],
                               values=['Age','Family size'],
                               aggfunc=[np.mean, np.median,len], margins=True)
delivery_pivot3


delivery_pivot4=pd.pivot_table(df,index=["Monthly Income"],
                               values=['Age','Family size'],
                               aggfunc=[np.mean, np.median,len], margins=True)
delivery_pivot4


delivery_pivot3_=pd.pivot_table(df,index=["Educational Qualifications","Occupation"],
                               values=['Age','Family size'],
                               aggfunc=[np.mean,len])

#Adding bar for numbers
delivery_pivot3_.style.bar()


import plotly.express as px


fig = px.histogram(df, x="Age")
fig.show()


fig = px.bar(df, x="Gender")
fig.show()


from matplotlib import pyplot as plt
import seaborn as sns


demog = ['Age', 'Gender', 'Marital Status',"Occupation", 'Monthly Income', "Educational Qualifications", 'Family size']


_, axes = plt.subplots(1, len(demog), figsize=(35, 5))
for i, feat in enumerate(demog):
    sns.countplot(x=feat, data=df, ax=axes[i])
plt.show()


peref= ['Medium (P1)', 'Medium (P2)', 'Meal(P1)', 'Meal(P2)', 'Perference(P1)', 'Perference(P2)']


_, axes = plt.subplots(1, len(peref), figsize=(35, 5))
for i, feat in enumerate(peref):
    sns.countplot(x=feat, data=df, ax=axes[i])
plt.show()


satis = ['Ease and convenient', 'Time saving', 'More restaurant choices', 'Easy Payment option', 'More Offers and Discount', 'Good Food quality', 'Good Tracking system']


_, axes = plt.subplots(1, len(satis), figsize=(35, 5))
for i, feat in enumerate(satis):
    sns.countplot(x=feat, data=df, ax=axes[i])
plt.show()


purc = ['Self Cooking', 'Health Concern', 'Late Delivery', 'Poor Hygiene', 'Bad past experience', 'Unavailability', 'Unaffordable']


_, axes = plt.subplots(1, len(purc), figsize=(80, 5))
for i, feat in enumerate(purc):
    sns.countplot(x=feat, data=df, ax=axes[i])
plt.show()


canc = ['Long delivery time', 'Delay of delivery person getting assigned', 'Delay of delivery person picking up food', 'Wrong order delivered', 'Missing item', 'Order placed by mistake']


_, axes = plt.subplots(1, len(canc), figsize=(80, 5))
for i, feat in enumerate(canc):
    sns.countplot(x=feat, data=df, ax=axes[i])
plt.show()


perf = ['Influence of time', 'Order Time', 'Maximum wait time', 'Residence in busy location', 'Google Maps Accuracy', 'Good Road Condition', 'Low quantity low time', 'Delivery person ability']


_, axes = plt.subplots(1, len(perf), figsize=(80, 5))
for i, feat in enumerate(perf):
    sns.countplot(x=feat, data=df, ax=axes[i])
plt.show()


loyl = ['Influence of rating', 'Less Delivery time', 'High Quality of package', 'Number of calls', 'Politeness', 'Freshness ', 'Temperature', 'Good Taste ', 'Good Quantity', 'Output']


_, axes = plt.subplots(1, len(loyl), figsize=(80, 5))
for i, feat in enumerate(loyl):
    sns.countplot(x=feat, data=df, ax=axes[i])
plt.show()


# 1. Gender vs Good Food quality
sns.countplot(x='Gender', hue='Good Food quality', data=df)

<AxesSubplot:xlabel='Gender', ylabel='count'>


import plotly.express as px


# 1. Gender vs Good Food quality
fig = px.sunburst(data_frame=df, path=('Gender', 'Good Food quality'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 2. Gender vs self cooking
fig = px.sunburst(data_frame=df, path=('Gender', 'Self Cooking'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 3. Marital status vs self cooking
fig = px.sunburst(data_frame=df, path=('Marital Status', 'Self Cooking'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 4. Monthly income vs Health concern
fig = px.sunburst(data_frame=df, path=('Monthly Income', 'Health Concern'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 5. Occupation vs Poor Hygiene
fig = px.sunburst(data_frame=df, path=('Occupation', 'Poor Hygiene'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 6. Family size vs Bad past experience
fig = px.sunburst(data_frame=df, path=('Family size', 'Bad past experience'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 7. Educational Qualifications vs Poor Hygiene
fig = px.sunburst(data_frame=df, path=('Educational Qualifications', 'Poor Hygiene'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 8. Gender vs Long Delivery Time
fig = px.sunburst(data_frame=df, path=('Gender', 'Long delivery time'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 9. Age vs Long Order delivered
fig = px.sunburst(data_frame=df, path=('Age', 'Wrong order delivered'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 10. Occupation vs Missing item
fig = px.sunburst(data_frame=df, path=('Occupation', 'Missing item'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 11. Monthly Income vs Order Placed by mistake
fig = px.sunburst(data_frame=df, path=('Monthly Income', 'Order placed by mistake'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 12. Family Size vs Influence of time
fig = px.sunburst(data_frame=df, path=('Family size', 'Influence of time'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 13. Occupation vs Influence of time
fig = px.sunburst(data_frame=df, path=('Occupation', 'Influence of time'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 14. Marital Status vs Output
fig = px.sunburst(data_frame=df, path=('Marital Status', 'Output'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 15. Monthly Income vs More Offers and Discount
fig = px.sunburst(data_frame=df, path=('Monthly Income', 'More Offers and Discount'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 16. Monthly Income vs Good Food quality
fig = px.sunburst(data_frame=df, path=('Monthly Income', 'Good Food quality'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 17. Monthly Income vs Unaffordable
fig = px.sunburst(data_frame=df, path=('Monthly Income', 'Unaffordable'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 18. Family size vs Unavailability
fig = px.sunburst(data_frame=df, path=('Family size', 'Unavailability'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 19. Gender vs Unavailability
fig = px.sunburst(data_frame=df, path=('Gender', 'Unavailability'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


# 20. Gender vs Maximum wait time
fig = px.sunburst(data_frame=df, path=('Gender', 'Maximum wait time'))
fig.update_traces(textinfo='label+percent parent')
fig.show()


import pandas as pd
dd = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')

dd.head(4)


df.head(4)


import plotly.express as px
fig = px.density_mapbox(df, lat='latitude', lon='longitude', z='Age', radius=10,
                        center=dict(lat=0, lon=180), zoom=0,
                        mapbox_style="stamen-terrain")
fig.show()


df['max_wait_min'] = df['Maximum wait time'].str.replace("minutes","").str.replace("more than","")
df['max_wait_min']

0      30 
1      30 
2      45 
3      30 
4      30 
      ... 
383    30 
384    45 
385    45 
386    45 
387    30 
Name: max_wait_min, Length: 388, dtype: object


df.head(5)


import plotly.express as px
fig = px.density_mapbox(df, lat='latitude', lon='longitude', z='max_wait_min', radius=10,
                        center=dict(lat=0, lon=180), zoom=0,
                        mapbox_style="stamen-terrain")
fig.show()


#----------------------------# Load Your Dependencies#--------------------------#
import dash
from jupyter_dash import JupyterDash  # jupyter dash helps you to deploy dash on jupyter lab
from dash import  dcc    # Dash core Components
from dash import html   # HTML for Layout and Fonts
import plotly.express as px           # Plotly Graphs uses graph objects internally
from plotly.subplots import make_subplots
import plotly.graph_objects as go     # Plotly Graph  more customized 
import pandas as pd                   # Pandas For Data Wrangling
from dash import Input, Output, dash_table  # Input, Output for  Call back functions

#--------------------------#Instanitiate Your App#--------------------------#

app = JupyterDash(__name__)  

#--------------------------# Pandas Section #------------------------------#

df =pd.read_csv('onlinedeliverydata.csv')
group = ['Consumer demographics', 'Meal perefernces', 'Satisfaction', 'Not purchasing concerns', 'Cancellation concerns', 'Pereferences', 'Loyality']
demog = ['Age', 'Gender', 'Marital Status',"Occupation", 'Monthly Income', "Educational Qualifications", 'Family size']
peref= ['Medium (P1)', 'Medium (P2)', 'Meal(P1)', 'Meal(P2)', 'Perference(P1)', 'Perference(P2)']
satis = ['Ease and convenient', 'Time saving', 'More restaurant choices', 'Easy Payment option', 'More Offers and Discount', 'Good Food quality', 'Good Tracking system']
purc = ['Self Cooking', 'Health Concern', 'Late Delivery', 'Poor Hygiene', 'Bad past experience', 'Unavailability', 'Unaffordable']
canc = ['Long delivery time', 'Delay of delivery person getting assigned', 'Delay of delivery person picking up food', 'Wrong order delivered', 'Missing item', 'Order placed by mistake']
perf = ['Influence of time', 'Order Time', 'Maximum wait time', 'Residence in busy location', 'Google Maps Accuracy', 'Good Road Condition', 'Low quantity low time', 'Delivery person ability']
loyl = ['Influence of rating', 'Less Delivery time', 'High Quality of package', 'Number of calls', 'Politeness', 'Freshness ', 'Temperature', 'Good Taste ', 'Good Quantity', 'Output']
all_g = [demog, peref, satis, purc, canc, perf, loyl]
df_piv=pd.pivot_table(df,index=["Educational Qualifications","Occupation"], values=['Age','Family size'],
                               aggfunc=[np.median,len])
#df_piv.style.bar()
df_piv.reset_index().to_dict('rows')
#--------------------------------------------------------------------------#
    
app = JupyterDash(__name__)  

app.layout = html.Div([html.Div([html.A([html.H2('Food Delivery Analysis Dashboard'),html.Img(src='/assets/logo1.png')],  # A for hyper links
                                        href='http://projectnitrous.com/')],className="banner"),
                       # First raw
                       html.Div([
                           html.H4('Univariate Analysis for the data groups', style={'color':'#ffffff'}),
                       ], className="eleven columns", style={'padding':10}),
                       # Total Revenue
                       html.Div([
                           dcc.Dropdown(
                                id='dropdown_grp',
                                options=[{'label':group[i], 'value':i} for i in range(len(group))],
                                value=0,
                                multi=False,
                                searchable=True,
                                clearable=False
                           ),
                           html.Div(
                               dcc.Graph(id='hist_grp'),
                           ),
                       ], className="eleven columns", style={'backgroundColor': '#2a2b4a', 'padding':10}),
                       #, 'border-radius': 25
                       
                       html.Div([html.Br(),],className="eleven columns"),
                       # Second raw
                       html.Div([
                           html.H4('Multivariate Analysis', style={'color':'#ffffff'}),
                       ], className="five columns", style={'padding':10}),
                       html.Div([
                           html.H4('Multivariate Analysis', style={'color':'#ffffff'}),
                       ], className="five columns", style={'padding':10}),
                       # Multivariate Analysis
                       html.Div([
                           dcc.Dropdown(
                                id='dropdown_var1',
                                options=[{'label':i, 'value':i} for i in df.iloc[:,:7]],
                                value='Age',
                                multi=False,
                                searchable=True,
                                clearable=False
                           ),
                           dcc.Dropdown(
                                id='dropdown_var2',
                                options=[{'label':i, 'value':i} for i in df.iloc[:,10:-2]],
                                value='Medium (P1)',
                                multi=False,
                                searchable=True,
                                clearable=False
                           ),
                           html.Div(
                               dcc.Graph(id='sun_multi1'),
                           ),
                       ], className="five columns", style={'backgroundColor': '#2a2b4a', 'padding':10}),
                       # Multivariate Analysis
                       html.Div([
                           dcc.Dropdown(
                                id='dropdown_var3',
                                options=[{'label':i, 'value':i} for i in df[:7]],
                                value='Age',
                                multi=False,
                                searchable=True,
                                clearable=False
                           ),
                           dcc.Dropdown(
                                id='dropdown_var4',
                                options=[{'label':i, 'value':i} for i in df[11:-2]],
                                value='Medium (P1)',
                                multi=False,
                                searchable=True,
                                clearable=False
                           ),
                           html.Div(
                               dcc.Graph(id='sun_multi2'),
                           ),
                       ], className="five columns", style={'backgroundColor': '#2a2b4a', 'padding':10}),
                       
                       html.Div([html.Br(),],className="eleven columns"),
#                        # Third raw
#                        html.Div([
#                            html.H4('Total quantity/store type & products', style={'color':'#003595'}),
#                        ], className="three columns", style={'padding':10}),
#                        html.Div([
#                            html.H4('Total quantity/store type & gender', style={'color':'#003595'}),
#                        ], className="three columns", style={'padding':10}),
#                        html.Div([
#                            html.H4('Total quantity/store type & age', style={'color':'#003595'}),
#                        ], className="three columns", style={'padding':10}),
#                        # Store type vs Products and Sub-Products
#                        html.Div([
#                            dcc.Dropdown(
#                                 id='dropdown_prod',
#                                 options=[
#                                     {'label': 'Store type', 'value': 'Store_type'},
#                                     {'label': 'Product', 'value': 'prod_cat'},
#                                     {'label': 'Sub Product', 'value': 'prod_subcat'},
#                                 ],

#                                 value=['Store_type'],
#                                 multi=True,
#                                 searchable=True,
#                                 clearable=False
#                            ),
#                            html.Div(
#                                dcc.Graph(id='sun_prod'),
#                            ),
#                        ], className="three columns", style={'backgroundColor': '#efedfa', 'border-radius': 25, 'padding':10}),
#                        # Store type vs Gender
#                        html.Div([
#                            dcc.Dropdown(
#                                 id='dropdown_gen',
#                                 options=[
#                                     {'label': 'Store type', 'value': 'Store_type'},
#                                     {'label': 'Gender', 'value': 'Gender'},
#                                 ],

#                                 value=['Store_type'],
#                                 multi=True,
#                                 searchable=True,
#                                 clearable=False
#                            ),
#                            html.Div(
#                                dcc.Graph(id='sun_gen'),
#                            ),
#                        ], className="three columns", style={'backgroundColor': '#efedfa', 'border-radius': 25, 'padding':10}),
#                        # Store type vs Age
#                        html.Div([
#                            dcc.Dropdown(
#                                 id='dropdown_age',
#                                 options=[
#                                     {'label': 'Store type', 'value': 'Store_type'},
#                                     {'label': 'Age category', 'value': 'age_cat'},
#                                 ],

#                                 value=['Store_type'],
#                                 multi=True,
#                                 searchable=True,
#                                 clearable=False
#                            ),
#                            html.Div(
#                                dcc.Graph(id='sun_age'),
#                            ),
#                        ], className="three columns", style={'backgroundColor': '#efedfa', 'border-radius': 25, 'padding':10}),
                       
#                        html.Div([html.Br(),],className="eleven columns"),
#                        # Fourth raw
#                        html.Div([
#                            html.H4('Customers count per city', style={'color':'#003595'}),
#                        ], className="five columns", style={'padding':10}),
#                        html.Div([
#                            html.H4('Correlations between different fields affecting the clients', style={'color':'#003595'}),
#                        ], className="five columns", style={'padding':10}),
#                        # City & No. of Customers
#                        html.Div([
#                                dcc.Graph(id='hist_city'),
#                        ], className="five columns", style={'backgroundColor': '#efedfa', 'border-radius': 25, 'padding':10}),
#                        # Correlation Heatmap
#                        html.Div([
#                            dcc.Dropdown(
#                                 id='dropdown_cor',
#                                 options=[{'label':df.columns[i], 'value':df.columns[i]} for i in range(len(df.columns)) if (df[df.columns[i]].dtype== 'int64') or (df[df.columns[i]].dtype== 'float64')],

#                                 value=['Qty', 'Rate'],
#                                 multi=True,
#                                 searchable=True,
#                                 clearable=False
#                            ),
#                            html.Div(
#                                dcc.Graph(id='heatmap_cor'),
#                            ),
#                        ], className="five columns", style={'backgroundColor': '#efedfa', 'border-radius': 25, 'padding':10}),
                       
#                        html.Div([html.Br(),],className="eleven columns"),
#                        # Fifth raw
#                        # Sentences
                       html.Div([
                           html.H4('The most and least values', style={'color':'#ffffff'}),
                       ], className="eleven columns", style={'padding':10}),
                       html.Div([
                           # dcc.RadioItems(
                           #     id='radio_value',
                           #      options=[
                           #          {'label': 'Highest value', 'value': 'most'},
                           #          {'label': 'Lowest value', 'value': 'least'}
                           #      ],
                           #      value='most',
                           #      labelStyle={'display': 'inline-block'}
                           # ),
                           # html.H4(html.Div(id='sent1')),
                           html.Div(
                               #dash_table.DataTable(df_piv.to_dict('records'), [{"name": i, "id": i} for i in df_piv.columns]),
                           ),
                       ], className="eleven columns", style={'backgroundColor': '#2a2b4a', 'padding':10}),
               ], className="twelve columns", style={'backgroundColor': '#1b203d', 'width':'100%', 'height':'100%', 'top':'0px', 'left':'0px'})

@app.callback(
    Output('hist_grp', 'figure'),
    Input('dropdown_grp', 'value'),
    )
def grp_hist(value):
    for i in range(len(all_g)):
        if i == int(value):
            col_len = len(all_g[i])
            fig = make_subplots(rows=1, cols=col_len)
            for x in range(1, col_len+1):
                x_val=str(all_g[i][x-1])
                fig.add_trace(px.histogram(df, x=x_val, barmode='group', labels=x_val, text_auto=True).data[0], row=1, col=x)
            fig.update_layout({'paper_bgcolor': 'rgba(0, 0, 0, 0)'})
    return fig

@app.callback(
    Output('sun_multi1', 'figure'),
    Input('dropdown_var1', 'value'),
    Input('dropdown_var2', 'value'),
    )
def multi1_sun(value1, value2):
    fig = px.sunburst(data_frame=df, path=(value1, value2))
    fig.update_traces(textinfo='label+percent parent')
    fig.update_layout({'paper_bgcolor': 'rgba(0, 0, 0, 0)'})
    return fig

@app.callback(
    Output('sun_multi2', 'figure'),
    Input('dropdown_var3', 'value'),
    Input('dropdown_var4', 'value'),
    )
def multi2_sun(value1, value2):
    fig = px.sunburst(data_frame=df, path=(value1, value2))
    fig.update_traces(textinfo='label+percent parent')
    fig.update_layout({'paper_bgcolor': 'rgba(0, 0, 0, 0)'})
    return fig

# @app.callback(
#     Output('pv_table', 'children'),
#     Input('pv_table', 'id'),
#     )
# def table_pv(value):
#     df_piv=pd.pivot_table(df,index=["Educational Qualifications","Occupation"], values=['Age','Family size'],
#                                aggfunc=[np.median,len])
#     df_piv.style.bar()
#     dash_piv = dash_table.DataTable(df_piv.to_dict('records'), [{"name": i, "id": i} for i in df_piv.columns])
#     return dash_piv

app.run_server(mode='external')

C:\Users\Monmon\AppData\Local\Temp/ipykernel_11352/2200546115.py:31: FutureWarning:

Using short name for 'orient' is deprecated. Only the options: ('dict', list, 'series', 'split', 'records', 'index') will be used in a future version. Use one of the above to silence this warning.

	Age	Gender	Marital Status	Occupation	Monthly Income	Educational Qualifications	Family size	latitude	longitude	Pin code	...	Less Delivery time	High Quality of package	Number of calls	Politeness	Freshness	Temperature	Good Taste	Good Quantity	Output	Reviews
0	20	Female	Single	Student	No Income	Post Graduate	4	12.9766	77.5993	560001	...	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Yes	Nil\n
1	24	Female	Single	Student	Below Rs.10000	Graduate	3	12.9770	77.5773	560009	...	Very Important	Very Important	Very Important	Very Important	Very Important	Very Important	Very Important	Very Important	Yes	Nil
2	22	Male	Single	Student	Below Rs.10000	Post Graduate	3	12.9551	77.6593	560017	...	Important	Very Important	Moderately Important	Very Important	Very Important	Important	Very Important	Moderately Important	Yes	Many a times payment gateways are an issue, so...
3	22	Female	Single	Student	No Income	Graduate	6	12.9473	77.5616	560019	...	Very Important	Important	Moderately Important	Very Important	Very Important	Very Important	Very Important	Important	Yes	nil
4	22	Male	Single	Student	Below Rs.10000	Post Graduate	4	12.9850	77.5533	560010	...	Important	Important	Moderately Important	Important	Important	Important	Very Important	Very Important	Yes	NIL

	Age	Gender	Marital Status	Occupation	Monthly Income	Educational Qualifications	Family size	latitude	longitude	Pin code	Medium (P1)	Medium (P2)	Meal(P1)	Meal(P2)	Perference(P1)	Perference(P2)	Ease and convenient	Time saving	More restaurant choices	Easy Payment option	More Offers and Discount	Good Food quality	Good Tracking system	Self Cooking	Health Concern	Late Delivery	Poor Hygiene	Bad past experience	Unavailability	Unaffordable	Long delivery time	Delay of delivery person getting assigned	Delay of delivery person picking up food	Wrong order delivered	Missing item	Order placed by mistake	Influence of time	Order Time	Maximum wait time	Residence in busy location	Google Maps Accuracy	Good Road Condition	Low quantity low time	Delivery person ability	Influence of rating	Less Delivery time	High Quality of package	Number of calls	Politeness	Freshness	Temperature	Good Taste	Good Quantity	Output	Reviews
0	20	Female	Single	Student	No Income	Post Graduate	4	12.9766	77.5993	560001	Food delivery apps	Web browser	Breakfast	Lunch	Non Veg foods (Lunch / Dinner)	Bakery items (snacks)	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral	Agree	Agree	Agree	Agree	Agree	Agree	Yes	Weekend (Sat & Sun)	30 minutes	Agree	Neutral	Neutral	Neutral	Neutral	Yes	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Moderately Important	Yes	Nil\n
1	24	Female	Single	Student	Below Rs.10000	Graduate	3	12.9770	77.5773	560009	Food delivery apps	Web browser	Snacks	Dinner	Non Veg foods (Lunch / Dinner)	Veg foods (Breakfast / Lunch / Dinner)	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Neutral	Agree	Strongly agree	Strongly agree	Agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Yes	Anytime (Mon-Sun)	30 minutes	Strongly Agree	Neutral	Disagree	Strongly disagree	Agree	Yes	Very Important	Very Important	Very Important	Very Important	Very Important	Very Important	Very Important	Very Important	Yes	Nil
2	22	Male	Single	Student	Below Rs.10000	Post Graduate	3	12.9551	77.6593	560017	Food delivery apps	Direct call	Lunch	Snacks	Non Veg foods (Lunch / Dinner)	Ice cream / Cool drinks	Strongly agree	Strongly agree	Strongly agree	Neutral	Neutral	Disagree	Neutral	Disagree	Neutral	Neutral	Agree	Agree	Agree	Agree	Agree	Agree	Agree	Strongly agree	Agree	Neutral	Yes	Anytime (Mon-Sun)	45 minutes	Agree	Strongly Agree	Neutral	Neutral	Agree	Yes	Important	Very Important	Moderately Important	Very Important	Very Important	Important	Very Important	Moderately Important	Yes	Many a times payment gateways are an issue, so...
3	22	Female	Single	Student	No Income	Graduate	6	12.9473	77.5616	560019	Food delivery apps	Walk-in	Snacks	Dinner	Veg foods (Breakfast / Lunch / Dinner)	Bakery items (snacks)	Agree	Agree	Strongly agree	Agree	Strongly agree	Agree	Agree	Agree	Strongly agree	Neutral	Agree	Disagree	Disagree	Neutral	Agree	Agree	Agree	Disagree	Disagree	Neutral	Yes	Anytime (Mon-Sun)	30 minutes	Disagree	Agree	Agree	Neutral	Agree	Yes	Very Important	Important	Moderately Important	Very Important	Very Important	Very Important	Very Important	Important	Yes	nil
4	22	Male	Single	Student	Below Rs.10000	Post Graduate	4	12.9850	77.5533	560010	Walk-in	Direct call	Lunch	Dinner	Non Veg foods (Lunch / Dinner)	Veg foods (Breakfast / Lunch / Dinner)	Agree	Agree	Agree	Agree	Agree	Neutral	Neutral	Agree	Strongly agree	Strongly agree	Agree	Strongly agree	Agree	Disagree	Strongly agree	Strongly agree	Neutral	Neutral	Neutral	Disagree	Yes	Weekend (Sat & Sun)	30 minutes	Agree	Agree	Agree	Agree	Agree	Yes	Important	Important	Moderately Important	Important	Important	Important	Very Important	Very Important	Yes	NIL

	Age	Family size	latitude	longitude	Pin code
count	388.000000	388.000000	388.000000	388.000000	388.000000
mean	24.628866	3.280928	12.972058	77.600160	560040.113402
std	2.975593	1.351025	0.044489	0.051354	31.399609
min	18.000000	1.000000	12.865200	77.484200	560001.000000
25%	23.000000	2.000000	12.936900	77.565275	560010.750000
50%	24.000000	3.000000	12.977000	77.592100	560033.500000
75%	26.000000	4.000000	12.997025	77.630900	560068.000000
max	33.000000	6.000000	13.102000	77.758200	560109.000000

		mean		len
		Age	Family size	Age	Family size
Gender	Marital Status
Female	Married	27.265306	3.653061	49	49
	Prefer not to say	26.000000	4.000000	5	5
	Single	23.098214	3.276786	112	112
Male	Married	28.016949	3.864407	59	59
	Prefer not to say	27.285714	1.571429	7	7
	Single	23.455128	3.000000	156	156
All		24.628866	3.280928	388	388

	median		len
	Age	Family size	Age	Family size
Gender
Female	24.0	3	166	166
Male	24.5	3	222	222
All	24.0	3	388	388

6_ Food Delivery Data Analysis¶

Import Libraries¶

About Data¶

Univariate Analysis¶

Multivariate Analysis¶

Geographical Analysis¶

Draw Scatter Mapbox based on `Maximum Wait time`¶

Build A Data Story¶

Build A Dashboard¶

	Date	Latitude	Longitude	Magnitude
0	01/02/1965	19.246	145.616	6.0
1	01/04/1965	1.863	127.352	5.8
2	01/05/1965	-20.579	-173.972	6.2
3	01/08/1965	-59.076	-23.557	5.8

		mean		median		len
		Age	Family size	Age	Family size	Age	Family size
Occupation	Monthly Income
Employee	10001 to 25000	24.304348	3.086957	24	3	23	23
	25001 to 50000	26.750000	3.134615	27	3	52	52
	Below Rs.10000	26.250000	2.625000	26	2	8	8
	More than 50000	27.885714	3.771429	27	4	35	35
House wife	No Income	30.333333	4.777778	31	5	9	9
Self Employeed	10001 to 25000	26.066667	3.266667	26	3	15	15
	25001 to 50000	26.000000	3.857143	26	3	14	14
	More than 50000	26.800000	3.480000	26	3	25	25
Student	10001 to 25000	23.714286	3.571429	24	3	7	7
	25001 to 50000	23.000000	2.333333	23	3	3	3
	Below Rs.10000	22.764706	3.058824	22	3	17	17
	More than 50000	23.000000	3.000000	23	3	2	2
	No Income	22.775281	3.162921	23	3	178	178
All		24.628866	3.280928	24	3	388	388

		mean		len
		Age	Family size	Age	Family size
Educational Qualifications	Occupation
Graduate	Employee	26.514706	3.250000	68	68
	House wife	31.666667	4.333333	3	3
	Self Employeed	26.655172	3.862069	29	29
	Student	21.987013	3.038961	77	77
Ph.D	Employee	28.000000	4.083333	12	12
	Self Employeed	24.666667	3.000000	3	3
	Student	25.250000	3.375000	8	8
Post Graduate	Employee	26.236842	3.078947	38	38
	Self Employeed	25.571429	2.785714	14	14
	Student	23.172131	3.213115	122	122
School	House wife	29.200000	5.400000	5	5
School	Self Employeed	27.142857	3.714286	7	7
Uneducated	House wife	32.000000	3.000000	1	1
Uneducated	Self Employeed	30.000000	4.000000	1	1

6_ Food Delivery Data Analysis¶

Import Libraries¶

About Data¶

Univariate Analysis¶

Multivariate Analysis¶

Geographical Analysis¶

Draw Scatter Mapbox based on Maximum Wait time¶

Build A Data Story¶

Build A Dashboard¶

Draw Scatter Mapbox based on `Maximum Wait time`¶